Roberto Baccoli

Comparison between an artificial neural network and logistic regression in predicting acute graft-vs-host disease after unrelated donor hematopoietic stem cell transplantation in thalassemia patients.

OBJECTIVE There is growing interest in the development of prognostic models for predicting the occurrence of acute graft-vs-host disease (aGVHD) after unrelated donor hematopoietic stem cell transplantation. A high number of variables have been shown to play a role in aGVHD, but the search for a predictive algorithm is still ongoing. Artificial neural networks (ANNs) represent an attractive alternative to multivariate analysis for clinical prognosis. So far, no reports have investigated the ability of ANNs in predicting HSCT outcome. MATERIALS AND METHODS We compared the prognostic performance of ANNs with that of logistic regression (LR) in 78 beta-thalassemia major patients given unrelated donor hematopoietic stem cell transplantation. Twenty-four independent variables were analyzed for their potential impact on outcomes. RESULTS Twenty-six patients (33.3%) developed grade II to IV aGVHD. In multivariate analysis, homozygosity for donor KIR haplotype A (p = 0.03), donor age (p = 0.05), and donor homozygosity for the deletion of the human leukocyte antigen-G 14-bp polymorphism (p = 0.05) were independently significantly correlated to aGVHD. The mean sensitivity of LR and ANNs (capability of predicting aGVHD in patients who developed aGVHD) in test datasets was 21.7% and 83.3%, respectively (p < 0.001); the mean specificity (capability of predicting absence of aGVHD in patients who did not develop aGVHD) was 80.5% and 90.1%, respectively (p = NS). CONCLUSION Although ANNs are unable to calculate the weight of single variables on outcomes, they were found to have a better performance than LR. A combination of these two methods could be more efficient in predicting outcomes and help tailor GVHD prophylaxis regimens according to the predicted risk of each patient. Whether ANN technology will provide better predictive performance when applied to other datasets remains to be confirmed.

Comparison Between an Artificial Neural Network and Logistic Regression in Predicting Long Term Kidney Transplantation Outcome

Predicting clinical outcome following a specific treatment is a challenge that sees physicians and researchers alike sharing the dream of a crystal ball to read into the future. In Medicine, several tools have been developed for the prediction of outcomes following drug treatment and other medical interventions. The standard approach for a binary outcome is to use logistic regression (LR) [1,2] but over the past few years artificial neural networks (ANNs) have become an increas‐ ingly popular alternative to LR analysis for prognostic and diagnostic classification in clinical medicine [3]. The growing interest in ANNs has mainly been triggered by their ability to mimic the learning processes of the human brain. The network operates in a feed-forward mode from the input layer through the hidden layers to the output layer. Exactly what interactions are mod‐ eled in the hidden layers is still under study. Each layer within the network is made up of com‐ puting nodes with remarkable data processing abilities. Each node is connected to other nodes of a previous layer through adaptable inter-neuron connection strengths known as synaptic weights. ANNs are trained for specific applications through a learning process and knowledge is usually retained as a set of connection weights [4]. The backpropagation algorithm and its var‐ iants are learning algorithms that are widely used in neural networks. With backpropagation, the input data is repeatedly presented to the network. Each time, the output is compared to the desired output and an error is computed. The error is then fed back through the network and used to adjust the weights in such a way that with each iteration it gradually declines until the neural model produces the desired output.

The Usefulness of Artificial Neural Networks in Predicting the Outcome of Hematopoietic Stem Cell Transplantation

1.1 Artificial neural networks in clinical medicine In Medicine, several tools have been developed for the prediction of clinical outcomes following drug treatment and other medical interventions. The standard approach for a binary outcome is to use logistic regression (LR), however, this method requires formal training and a profound knowledge of statistics (Royston, 2000; Harrel et al., 1996). LR is used to predict a categorical (usually dichotomous) variable from e set of predictor variables; it has been especially popular with medical research in which the dependent variable is whether or not a patient has a disease. Over the past years, artificial neural networks (ANNs) have increasingly been used as an alternative to LR analysis for prognostic and diagnostic classification in clinical medicine (Schwarzer et al., 2000). ANNs are composed of simple elements operating in parallel inspired by biological nervous systems. As in nature, the network function is determined largely by the connections between elements. After training with retrospective data ANNs are capable of making intelligent predictions given new, limited information. The growing interest in ANNs has mainly been triggered by their ability to mimic the learning processes of the human brain. However, the issue remains as to how these ANNs actually succeed in recognizing patterns within data that are too complex for the human brain. From here derives the so-called “black-box” aspect of ANNs. The network operates in a feed-forward mode from the input layer through the hidden layers (like in a black box) to the output layer. Exactly what interactions are modeled in the hidden layers is still a knot that remains untied. Each layer within the network is made up of computing nodes with remarkable data processing abilities. Each node is connected to other nodes of a previous layer through adaptable inter-neuron connection strengths known as synaptic weights. ANNs are trained for specific applications, such as pattern recognition or data classification, through a learning process and knowledge is usually retained as a set of connection weights. The backpropagation algorithm and its variants are learning algorithms that are widely used in neural networks. With backpropagation, the input data is repeatedly presented to the